Segmentation of moving objects in video sequences has many applications such as video surveillance, traffic monitoring, and object-based video coding. In some scenarios, the video background is static making segmentation easier than for those cases with moving backgrounds. Segmentation of objects with globally moving backgrounds poses a more complex problem than the static background case.
Many video segmentation methods attempt to identify foreground objects by subtracting the background in an image. In these cases, an accurate background model is needed to get reliable results. Some models estimate the background through a set of pixel values, using a running average, selective running average, or running Gaussian distribution. Median filtering of spatial pixels is also used to form a background model. Additionally, spatial correlations of pixel values are performed and consensus sample methods are used to generate stable background models. Performance for each of these types of methods varies with the content and becomes less reliable with globally moving backgrounds. Some existing segmentation techniques introduce artifacts and lose background detail.
Some compressed domain techniques are used for object segmentation, although some use just the dc value of a block and suffer from having block resolution. Another problem with compressed domain solutions is the difficulty of integrating their results with spatial domain imaging equipment.